Differentiated pattern of complement system activation between MOG-IgG-associated disease and AQP4-IgG-positive neuromyelitis optica spectrum disorder.

Background: Myelin oligodendrocyte glycoprotein antibody (MOG) immunoglobulin G (IgG)-associated disease (MOGAD) has clinical and pathophysiological features that are similar to but distinct from those of aquaporin-4 antibody (AQP4-IgG)-positive neuromyelitis optica spectrum disorders (AQP4-NMOSD). MOG-IgG and AQP4-IgG, mostly of the IgG1 subtype, can both activate the complement system. Therefore, we investigated whether the levels of serum complement components, regulators, and activation products differ between MOGAD and AQP4-NMOSD, and if complement analytes can be utilized to differentiate between these diseases. Methods: The sera of patients with MOGAD (from during an attack and remission; N=19 and N=9, respectively) and AQP4-NMOSD (N=35 and N=17), and healthy controls (N=38) were analyzed for C1q-binding circulating immune complex (CIC-C1q), C1 inhibitor (C1-INH), factor H (FH), C3, iC3b, and soluble terminal complement complex (sC5b-9). Results: In attack samples, the levels of C1-INH, FH, and iC3b were higher in the MOGAD group than in the NMOSD group (all, p<0.001), while the level of sC5b-9 was increased only in the NMOSD group. In MOGAD, there were no differences in the concentrations of complement analytes based on disease status. However, within AQP4-NMOSD, remission samples indicated a higher C1-INH level than attack samples (p=0.003). Notably, AQP4-NMOSD patients on medications during attack showed lower levels of iC3b (p<0.001) and higher levels of C3 (p=0.008), C1-INH (p=0.004), and sC5b-9 (p<0.001) compared to those not on medication. Among patients not on medication at the time of attack sampling, serum MOG-IgG cell-based assay (CBA) score had a positive correlation with iC3b and C1-INH levels (rho=0.764 and p=0.010, and rho=0.629 and p=0.049, respectively), and AQP4-IgG CBA score had a positive correlation with C1-INH level (rho=0.836, p=0.003). Conclusions: This study indicates a higher prominence of complement pathway activation and subsequent C3 degradation in MOGAD compared to AQP4-NMOSD. On the other hand, the production of terminal complement complexes (TCC) was found to be more substantial in AQP4-NMOSD than in MOGAD. These findings suggest a strong regulation of the complement system, implying its potential involvement in the pathogenesis of MOGAD through mechanisms that extend beyond TCC formation.

Regulatory role of miRNAs in the human immune and inflammatory response during the infection of SARS-CoV-2 and other respiratory viruses: A comprehensive review.

miRNAs are single-stranded ncRNAs that act as regulators of different human body processes. Several miRNAs have been noted to control the human immune and inflammatory response during severe acute respiratory infection syndrome (SARS-CoV-2) infection. Similarly, many miRNAs were upregulated and downregulated during different respiratory virus infections. Here, an attempt has been made to capture the regulatory role of miRNAs in the human immune and inflammatory response during the infection of SARS-CoV-2 and other respiratory viruses. Firstly, the role of miRNAs has been depicted in the human immune and inflammatory response during the infection of SARS-CoV-2. In this direction, several significant points have been discussed about SARS-CoV-2 infection, such as the role of miRNAs in human innate immune response; miRNAs and its regulation of granulocytes; the role of miRNAs in macrophage activation and polarisation; miRNAs and neutrophil extracellular trap formation; miRNA-related inflammatory response; and miRNAs association in adaptive immunity. Secondly, the miRNAs landscape has been depicted during human respiratory virus infections such as human coronavirus, respiratory syncytial virus, influenza virus, rhinovirus, and human metapneumovirus. The article will provide more understanding of the miRNA-controlled mechanism of the immune and inflammatory response during COVID-19, which will help more therapeutics discoveries to fight against the future pandemic.

Optimization of duplex digital PCR for the measurement of SARS-CoV-2 RNA.

Quantitative PCR (qPCR) is the gold standard for detecting nucleic acid sequences specific to the target pathogen. For COVID-19 diagnosis, several molecular assays have been developed. In this study, we present an optimization strategy for the measurement of SARS-CoV-2 RNA via multiplex qPCR and digital PCR (dPCR). Compared to qPCR, both droplet and chip-based dPCR, which are known to be more sensitive and accurate, showed a better resilience to suboptimal assay compositions and cycling conditions following the proposed optimizations. In particular, the formation of heterodimers among assays greatly interfered with qPCR results, but only minimally with dPCR results. In dPCR, existing heterodimers lowered the PCR efficiency, producing a dampened fluorescent signal in positive partitions. This can be corrected by adjusting the PCR cycling conditions, after which dPCR shows the capability of measuring the expected copy number. In addition, we present a process to improve the existing RdRp assay by correcting the primer sequences and matching the melting temperature, ultimately producing highly sensitive and robust assays. The results of this study can reduce the cost and time of SARS-CoV-2 diagnosis while increasing accuracy. Furthermore, our results suggest that dPCR is a reliable method for the accurate measurement of nucleic acid targets.

Current landscape of long COVID clinical trials.

Long COVID was reported as a multi-systemic condition after the infection of SARS-CoV-2, and more than 65 million people are suffering from this disease. It has been noted that around 10% of severe SARS-CoV-2 infected individuals are suffering from the enduring effects of long COVID. The symptoms of long COVID have also been noted in several mild or asymptomatic SARS-CoV-2 infected individuals. While limited reports on clinical trials investigating new therapeutics for long COVID exist, there is an abundance of scattered information available regarding these trials. This review explores the extensive literature search, and complete clinical trial database search to map the current status of long COVID clinical trials worldwide. The study listed about 110 long COVID clinical trials. In addition to conducting extensive long COVID clinical trials, we have comprehensively presented an overview of the condition, its symptoms, notable manifestations, associated clinical trials, the unique challenges it poses, and our recommendations for addressing long COVID.

Examining the Impact of the Current Reimbursement Regulation on Patient Access to Innovative Medical Devices in Taiwan: Insights From 8 Years' Reimbursement Data.

OBJECTIVES: This study aimed to assess the impact of the reimbursement regulation of medical devices (Regulation), introduced by the National Health Insurance Administration (NHIA) in 2013, on patients' access to innovative medical devices in Taiwan. METHODS: Analysis of the amount of time needed from application for NHIA reimbursement for new medical devices to receiving the decision from NHIA was done using the nonreimbursement product list featured on the NHIA website. Additionally, Welch analysis of variance was used to compare the amount of time it took from application to NHIA with reimbursement decisions made by the NHIA for different nonreimbursement code categories. Further, related Pharmaceutical Benefit Reimbursement Scheme meeting minutes were analyzed to obtain more detailed information concerning medical devices' reimbursement or not. RESULTS: From December 2012 to June 2021, the overall reimbursement percentage was 56.7%, and the average amount of time between application and reimbursement was 856.7 ± 474.7 days. The mandatory reimbursement rate was about 45%. NHIA reimbursement decisions as special medical devices also take a longer amount of time, because the applicants need to agree to the decision (P < .05). The NHIA decision-making process for nonreimbursement medical devices requires a significantly longer amount of time than for general materials (eg, suture, etc) decisions. CONCLUSIONS: Although the Regulation resolves payment issues, it also increases the amount of time to reach reimbursement decisions, thus hindering patient access to innovative medical devices. The study suggests that the review process needs to be simplified concerning reimbursement notification, using local real-world data to support reimbursement decisions.

Programming memristor arrays with arbitrarily high precision for analog computing.

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches.

Need an AI-Enabled, Next-Generation, Advanced ChatGPT or Large Language Models (LLMs) for Error-Free and Accurate Medical Information.

Recently, the interest in AI-guided ChatGPT has increased day-to-day, and different applications have been explored, including the medical field. The publication number is also increasing. At the same time, people are trying to get medical information from this Chartbot. However, researchers found that ChatGPT also provides partly correct or false information. Therefore, in this article, we urge the researchers to develop an AI-enabled, next-generation, advanced ChatGPT or large language models (LLMs) so that people can get accurate and error-free medical information.

Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.

Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.

A novel G3BP1-GFP reporter human lung cell system enabling real-time monitoring of stress granule dynamics for in vitro lung toxicity assessment.

Under various cellular stress conditions, including exposure to toxic chemicals, RNA-binding proteins (RBPs), including Ras GTPase-activating protein-binding protein 1 (G3BP1), aggregate and form stress granule complexes, which serve as hallmarks of cellular stress. The existing methods for analyzing stress granule assembly have limitations in the rapid detection of dynamic cellular stress and ignore the effects of constitutively overexpressed RBP on cellular stress and stress-related processes. Therefore, to overcome these limitations, we established a G3BP1-GFP reporter in a human lung epithelial cell line using CRISPR/Cas9-based knock-in as an alternative system for stress granule analysis. We showed that the G3BP1-GFP reporter system responds to stress conditions and forms a stress granule complex similar to that of native G3BP1. Furthermore, we validated the stress granule response of an established cell line under exposure to various household chemicals. Overall, this novel G3BP1-GFP reporter human lung cell system is capable of monitoring stress granule dynamics in real time and can be used for assessing the lung toxicity of various substances in vitro.

A Domain-Specific Next-Generation Large Language Model (LLM) or ChatGPT is Required for Biomedical Engineering and Research.

Large language models or ChatGPT have recently gained extensive media coverage. At the same time, the use of ChatGPT has increased deistically. Biomedical researchers, engineers, and clinicians have shown significant interest and started using it due to its diverse applications, especially in the biomedical field. However, it has been found that ChatGPT sometimes provided incorrect or partly correct information. It is unable to give the most recent information. Therefore, we urgently advocate a domain-specific next-generation, ChatBot for biomedical engineering and research, providing error-free, more accurate, and updated information. The domain-specific ChatBot can perform diversified functions in biomedical engineering, such as performing innovation in biomedical engineering, designing a medical device, etc. The domain-specific artificial intelligence enabled device will revolutionize biomedical engineering and research if a biomedical domain-specific ChatBot is produced.

ChatGPT and large language models in orthopedics: from education and surgery to research.

ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.

Current Status of Microneedle Array Technology for Therapeutic Delivery: From Bench to Clinic.

In recent years, microneedle (MN) patches have emerged as an alternative technology for transdermal delivery of various drugs, therapeutics proteins, and vaccines. Therefore, there is an urgent need to understand the status of MN-based therapeutics. The article aims to illustrate the current status of microneedle array technology for therapeutic delivery through a comprehensive review. However, the PubMed search was performed to understand the MN's therapeutics delivery status. At the same time, the search shows the number no of publications on MN is increasing (63). The search was performed with the keywords "Coated microneedle," "Hollow microneedle," "Dissolvable microneedle," and "Hydrogel microneedle," which also shows increasing trend. Similarly, the article highlighted the application of different microneedle arrays for treating different diseases. The article also illustrated the current status of different phases of MN-based therapeutics clinical trials. It discusses the delivery of different therapeutic molecules, such as drug molecule delivery, using microneedle array technology. The approach mainly discusses the delivery of different therapeutic molecules. The leading pharmaceutical companies that produce the microneedle array for therapeutic purposes have also been discussed. Finally, we discussed the limitations and future prospects of this technology.

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science.

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction.

A next-generation dynamic programming language Julia: Its features and applications in biological science.

BACKGROUND: The advent of Julia as a sophisticated and dynamic programming language in 2012 represented a significant milestone in computational programming, mathematical analysis, and statistical modeling. Having reached its stable release in version 1.9.0 on May 7, 2023, Julia has developed into a powerful and versatile instrument. Despite its potential and widespread adoption across various scientific and technical domains, there exists a noticeable knowledge gap in comprehending its utilization within biological sciences. THE AIM OF REVIEW: This comprehensive review aims to address this particular knowledge gap and offer a thorough examination of Julia's fundamental characteristics and its applications in biology. KEY SCIENTIFIC CONCEPTS OF THE REVIEW: The review focuses on a research gap in the biological science. The review aims to equip researchers with knowledge and tools to utilize Julia's capabilities in biological science effectively and to demonstrate the gap. It paves the way for innovative solutions and discoveries in this rapidly evolving field. It encompasses an analysis of Julia's characteristics, packages, and performance compared to the other programming languages in this field. The initial part of this review discusses the key features of Julia, such as its dynamic and interactive nature, fast processing speed, ease of expression manipulation, user-friendly syntax, code readability, strong support for multiple dispatch, and advanced type system. It also explores Julia's capabilities in data analysis, visualization, machine learning, and algorithms, making it suitable for scientific applications. The next section emphasizes the importance of using Julia in biological research, highlighting its seamless integration with biological studies for data analysis, and computational biology. It also compares Julia with other programming languages commonly used in biological research through benchmarking and performance analysis. Additionally, it provides insights into future directions and potential challenges in Julia's applications in biology.

Targeting Crosstalk of Signaling Pathways among Muscles-Bone-Adipose Tissue: A Promising Therapeutic Approach for Sarcopenia.

The aging process is associated with the development of a wide range of degenerative disorders in mammals. These diseases are characterized by a progressive decline in function at multiple levels, including the molecular, cellular, tissue, and organismal. Furthermore, it is responsible for various healthcare costs in developing and developed countries. Sarcopenia is the deterioration in the quality and functionality of muscles, which is extremely concerning as it manages many functions in the human body. This article reviews the molecular crosstalk involved in sarcopenia and the specific roles of many mediator molecules in establishing cross-talk between muscles, bone, and fatty tissues, eventually leading to sarcopenia. Besides, the involvement of various etiological factors, such as neurology, endocrinology, lifestyle, etc., makes it exceedingly difficult for clinicians to develop a coherent hypothesis that may lead to the well-organized management system required to battle this debilitating disease. The several hallmarks contributing to the progression of the disease is a vital question that needs to be addressed to ensure an efficient treatment for sarcopenia patients. Also, the intricate molecular mechanism involved in developing this disease requires more studies. The direct relationship of cellular senescence with aging is one of the pivotal issues contributing to disease pathophysiology. Some patented treatment strategies have been discussed, including drugs undergoing clinical trials and emerging options like miRNA and protein-enclosed extracellular vesicles. A clear understanding of the secretome, including the signaling pathways involved between muscles, bone, and fatty tissues, is extremely beneficial for developing novel therapeutics for curing sarcopenia.

Serum neurofilament and glial fibrillary acidic protein in idiopathic and seropositive transverse myelitis.

BACKGROUND: Serum levels of neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) reflect the disease activity and disability in central nervous system (CNS) demyelinating diseases. However, the clinical significance of NfL and GFAP in idiopathic transverse myelitis (iTM), an inflammatory spinal cord disease with unknown underlying causes, remains unclear. This study aimed to investigate NfL and GFAP levels in iTM and their association with the clinical parameters compared with those in TM with disease-specific antibodies such as anti-aquaporin 4 or myelin oligodendrocyte glycoprotein antibodies (sTM). METHODS: We collected serum and clinical data of 365 patients with CNS inflammatory diseases from 12 hospitals. The serum NfL and GFAP levels were measured in patients with iTM (n = 37) and sTM (n = 39) using ultrasensitive single-molecule array assays. Regression analysis was performed to investigate the associations between serum levels of NfL and GFAP and the clinical parameters such as higher EDSS scores (EDSS ≥ 4.0). RESULTS: Mean NfL levels were not significantly different between iTM (50.29 pg/ml) and sTM (63.18 pg/ml) (p = 0.824). GFAP levels were significantly lower in iTM (112.34 pg/ml) than in sTM (3814.20 pg/ml) (p = 0.006). NfL levels correlated with expanded disability status scale (EDSS) scores in sTM (p = 0.001) but not in iTM (p = 0.824). Disease duration also correlated with higher EDSS scores in sTM (p = 0.017). CONCLUSION: NfL levels and disease duration correlated with EDSS scores in sTM, and GFAP levels could be a promising biomarker to differentiate iTM from sTM.

Future Potential of Quantum Computing and Simulations in Biological Science.

The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference, quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.

Antibody evasion associated with the RBD significant mutations in several emerging SARS-CoV-2 variants and its subvariants.

Since the origin of the wild strain of SARS-CoV-2, several variants have emerged, which were designated as VOC, VOI, and VUM from time to time. The Omicron variant is noted as the recent VOC. After the origin of the Omicron variant on November 2021, several subvariants of Omicron have originated subsequently, like BA.1/2, BA.2.75/2.75.2, BA.4/5, BF.7, BQ.1/1.1, XBB.1/1.5, etc. which are circulated throughout the globe. Scientists reported that antibody escape is a common phenomenon observed in all the previous VOCs, VOIs, including Omicron and its subvariants. The mutations in the NTD (N-terminal domain) and RBD (Receptor-binding domain) of the spike of these variants and subvariants are responsible for antibody escape. At the same time, it has been noted that spike RBD mutations have been increasing in the last few months. This review illustrates significant RBD mutations namely R346T, K417N/T, L452R, N460K E484A/K/Q, and N501Y found in the previous emerging SARS-CoV-2 variants, including Omicron and its subvariants in high frequency and their role in antibody evasion and immune evasion. The review also describes the different classes of nAb responsible for antibody escape in SARS-CoV-2 variants and the molecular perspective of the mutation in nAb escape. It will help the future researchers to develop efficient vaccines which can finally prevent the pandemic.